from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.729	0.0	-1	1	0.047	0.002	0.253	0.254	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.908	0.0	-1	5	0.046	0.001	0.252	0.252	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.794	0.0	1	100	0.047	0.000	0.253	0.253	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.831	0.0	-1	100	0.046	0.000	0.253	0.253	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.884	0.0	1	5	0.047	0.000	0.249	0.249	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.663	0.0	1	1	0.047	0.000	0.255	0.255	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	-1	1	0.008	0.000	0.503	0.503	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.379	0.0	-1	5	0.008	0.000	0.509	0.510	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	1	100	0.008	0.000	0.509	0.509	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.381	0.0	-1	100	0.008	0.000	0.505	0.505	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.375	0.0	1	5	0.008	0.000	0.505	0.505	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	1	1	0.008	0.000	0.518	0.518	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.969	0.081	0.000	0.002	-1	1	0.175	0.002	11.230	11.231	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	0.000	0.023	-1	1	0.009	0.000	2.695	2.697	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.849	0.043	0.000	0.003	-1	5	0.180	0.003	15.807	15.809	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	0.000	0.025	-1	5	0.009	0.000	2.906	2.906	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.051	0.041	0.000	0.002	1	100	0.212	0.002	9.675	9.676	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.004	0.000	0.025	1	100	0.009	0.000	2.854	2.855	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.849	0.052	0.000	0.003	-1	100	0.212	0.003	13.418	13.419	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	0.000	0.025	-1	100	0.012	0.004	2.059	2.159	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.025	0.003	0.000	0.002	1	5	0.177	0.002	11.466	11.467	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.000	0.000	0.022	1	5	0.009	0.000	2.477	2.478	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.177	0.012	0.001	0.001	1	1	0.178	0.002	6.608	6.608	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	1	0.009	0.000	2.389	2.390	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.806	0.027	0.000	0.002	-1	1	0.026	0.000	68.351	68.361	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.005	0.000	0.006	-1	1	0.001	0.000	9.375	9.414	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.691	0.023	0.000	0.003	-1	5	0.027	0.001	98.270	98.299	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.003	0.000	0.006	-1	5	0.001	0.000	9.710	9.771	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.952	0.003	0.000	0.002	1	100	0.062	0.002	31.642	31.659	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.877	3.897	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.766	0.036	0.000	0.003	-1	100	0.060	0.001	45.792	45.794	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	0.000	0.007	-1	100	0.001	0.000	10.128	10.176	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.950	0.005	0.000	0.002	1	5	0.027	0.000	72.475	72.478	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.568	4.592	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.076	0.008	0.000	0.001	1	1	0.026	0.000	41.771	41.773	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	3.020	3.028	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.191	0.145	0.025	0.0	-1	1	0.802	0.070	3.980	3.995	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.730	0.104	0.021	0.0	-1	5	0.757	0.026	4.927	4.930	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.731	0.124	0.021	0.0	1	100	0.741	0.008	5.037	5.037	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.036	0.055	0.020	0.0	-1	100	0.745	0.005	5.421	5.421	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.750	0.041	0.021	0.0	1	5	0.742	0.015	5.054	5.055	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.710	0.037	0.022	0.0	1	1	0.747	0.012	4.963	4.964	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.020	0.0	-1	1	0.003	0.001	0.302	0.332	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	5	0.002	0.001	0.283	0.341	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.002	0.002	0.337	0.574	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.586	0.599	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	5	0.001	0.000	0.647	0.648	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.662	0.662	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.818	0.964	0.000	0.001	-1	1	0.121	0.003	6.784	6.787	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	-1	1	0.000	0.000	8.816	9.334	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.013	0.282	0.000	0.001	-1	5	0.217	0.003	4.665	4.666	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	8.357	8.901	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.678	0.240	0.000	0.006	1	100	0.643	0.005	8.832	8.832	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.001	0.000	4.819	5.110	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.289	0.184	0.000	0.003	-1	100	0.640	0.008	5.137	5.137	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.146	6.563	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.652	0.158	0.000	0.002	1	5	0.215	0.002	7.694	7.694	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	1	5	0.000	0.000	3.427	3.676	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.925	0.191	0.000	0.001	1	1	0.116	0.002	7.946	7.947	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.975	4.287	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.012	0.001	0.000	-1	1	0.000	0.000	65.013	65.259	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	24.747	25.850	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.002	0.001	0.000	-1	5	0.001	0.000	36.150	36.168	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	23.429	24.423	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.008	0.000	0.000	1	100	0.004	0.000	8.016	8.016	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.326	6.620	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.037	0.004	0.000	0.000	-1	100	0.005	0.001	7.130	7.405	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	21.361	22.584	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	31.857	31.886	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.567	6.859	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	48.581	48.664	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.540	6.920	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.511	0.068	30	0.031	0.0	random	0.410	0.027	1.245	1.247	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.565	0.015	30	0.028	0.0	k-means++	0.425	0.031	1.331	1.335	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.667	0.120	30	0.141	0.0	random	2.822	0.054	2.008	2.009	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.929	0.069	30	0.135	0.0	k-means++	3.016	0.019	1.966	1.966	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.010	0.000	random	0.0	0.0	8.475	12.769	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	9.642	14.174	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	11.788	13.566	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	12.079	13.821	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.486	0.000	random	0.0	0.0	6.396	6.864	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	12.995	13.431	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.473	0.000	k-means++	0.0	0.0	6.410	6.978	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	12.668	13.251	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.074	0.000	20	0.002	0.0	random	0.026	0.002	2.811	2.821	See	See
3	KMeans_short	fit	10000	10000	2	0.214	0.002	20	0.001	0.0	k-means++	0.081	0.000	2.651	2.651	See	See
6	KMeans_short	fit	10000	10000	100	0.197	0.003	20	0.041	0.0	random	0.108	0.001	1.815	1.815	See	See
9	KMeans_short	fit	10000	10000	100	0.569	0.009	20	0.014	0.0	k-means++	0.308	0.002	1.851	1.851	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.000	0.0	4.384	4.401	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.949	14.361	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	3.862	3.889	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.858	13.398	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.322	0.000	random	0.001	0.0	2.628	2.640	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.526	10.736	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.310	0.000	k-means++	0.001	0.0	2.787	2.800	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	11.754	11.933	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.316	0.495	[20]	0.071	0.000	2.030	0.036	5.576	5.577	See	See
3	LogisticRegression	fit	1000	1000	10000	0.961	0.575	[26]	0.083	0.001	1.001	0.030	0.960	0.961	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.262	0.0	0.001	0.001	0.573	1.063	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.013	0.0	0.000	0.000	0.449	0.457	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.078	0.0	0.005	0.001	0.288	0.291	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.125	0.0	0.001	0.000	0.056	0.058	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.191	0.004	0.419	0.0	0.182	0.001	1.045	1.045	See	See
3	Ridge	fit	1000000	1000000	100	1.141	0.065	0.701	0.0	0.308	0.255	3.700	4.800	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	6.551	0.0	0.019	0.0	0.646	0.646	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.345	0.0	0.000	0.0	0.649	0.713	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.919	0.0	0.000	0.0	0.492	0.658	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.017	0.0	0.000	0.0	0.585	0.619	See	See